labelImg 1.8.6

Get from PyPI but only python3.0 or above

This is the simplest (one-command) install method on modern Linux distributions such as Ubuntu and Fedora.

pip3installlabelImglabelImglabelImg[IMAGE_PATH][PRE-DEFINEDCLASSFILE]

Build from source

Linux/Ubuntu/Mac requires at least Python 2.6 and has been tested with PyQt 4.8. However, Python 3 or above and PyQt5 are strongly recommended.

sudoapt-getinstallpyqt5-dev-toolssudopip3install-rrequirements/requirements-linux-python3.txtmakeqt5py3python3labelImg.pypython3labelImg.py[IMAGE_PATH][PRE-DEFINEDCLASSFILE]

brewinstallqt# Install qt-5.x.x by Homebrew
brewinstalllibxml2orusingpippip3installpyqt5lxml# Install qt and lxml by pip
makeqt5py3python3labelImg.pypython3labelImg.py[IMAGE_PATH][PRE-DEFINEDCLASSFILE]

brewinstallpython3pip3installpipenvpipenvrunpipinstallpyqt5==5.15.2lxmlpipenvrunmakeqt5py3pipenvrunpython3labelImg.py[Optional]rm-rfbuilddist;pythonsetup.pypy2app-A;mv"dist/labelImg.app"/Applications

pyrcc4-olibs/resources.pyresources.qrcForpyqt5,pyrcc5-olibs/resources.pyresources.qrcpythonlabelImg.pypythonlabelImg.py[IMAGE_PATH][PRE-DEFINEDCLASSFILE]

condainstallpyqt=5condainstall-canacondalxmlpyrcc5-olibs/resources.pyresources.qrcpythonlabelImg.pypythonlabelImg.py[IMAGE_PATH][PRE-DEFINEDCLASSFILE]

Use Docker

dockerrun-it\
--user$(id-u)\
-eDISPLAY=unix$DISPLAY\
--workdir=$(pwd)\
--volume="/home/$USER:/home/$USER"\
--volume="/etc/group:/etc/group:ro"\
--volume="/etc/passwd:/etc/passwd:ro"\
--volume="/etc/shadow:/etc/shadow:ro"\
--volume="/etc/sudoers.d:/etc/sudoers.d:ro"\
-v/tmp/.X11-unix:/tmp/.X11-unix\
tzutalin/py2qt4makeqt4py2;./labelImg.py

You can pull the image which has all of the installed and required dependencies. Watch a demo video

Steps (PascalVOC)

1. Build and launch using the instructions above.

2. Click ‘Change default saved annotation folder’ in Menu/File

3. Click ‘Open Dir’

4. Click ‘Create RectBox’

5. Click and release left mouse to select a region to annotate the rect box

6. You can use right mouse to drag the rect box to copy or move it

The annotation will be saved to the folder you specify.

You can refer to the below hotkeys to speed up your workflow.

Steps (YOLO)

1. In data/predefined_classes.txt define the list of classes that will be used for your training.

2. Build and launch using the instructions above.

3. Right below “Save” button in the toolbar, click “PascalVOC” button to switch to YOLO format.

4. You may use Open/OpenDIR to process single or multiple images. When finished with a single image, click save.

A txt file of YOLO format will be saved in the same folder as your image with same name. A file named “classes.txt” is saved to that folder too. “classes.txt” defines the list of class names that your YOLO label refers to.

Note:

• Your label list shall not change in the middle of processing a list of images. When you save an image, classes.txt will also get updated, while previous annotations will not be updated.

• You shouldn’t use “default class” function when saving to YOLO format, it will not be referred.

• When saving as YOLO format, “difficult” flag is discarded.

Create pre-defined classes

You can edit the data/predefined_classes.txt to load pre-defined classes

Hotkeys

Verify Image:

When pressing space, the user can flag the image as verified, a green background will appear. This is used when creating a dataset automatically, the user can then through all the pictures and flag them instead of annotate them.

Difficult:

The difficult field is set to 1 indicates that the object has been annotated as “difficult”, for example, an object which is clearly visible but difficult to recognize without substantial use of context. According to your deep neural network implementation, you can include or exclude difficult objects during training.

How to reset the settings

In case there are issues with loading the classes, you can either:

1. From the top menu of the labelimg click on Menu/File/Reset All

2. Remove the .labelImgSettings.pkl from your home directory. In Linux and Mac you can do:

   rm ~/.labelImgSettings.pkl

How to contribute

Send a pull request

License

Free software: MIT license

Citation: Tzutalin. LabelImg. Git code (2015). https://github.com/tzutalin/labelImg

Related and additional tools

1. ImageNet Utils to download image, create a label text for machine learning, etc

2. Use Docker to run labelImg

3. Generating the PASCAL VOC TFRecord files

4. App Icon based on Icon by Nick Roach (GPL)

5. Setup python development in vscode

6. The link of this project on iHub platform

7. Convert annotation files to CSV format or format for Google Cloud AutoML

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